Using Synchronized Audio Mapping to Predict Velar and Pharyngeal Wall Locations during Dynamic MRI Sequences

Automatic tongue, velum (i.e., soft palate), and pharyngeal movement tracking systems provide a significant benefit for the analysis of dynamic speech movements. Studies have been conducted using ultrasound, x-ray, and Magnetic Resonance Images (MRI) to examine the dynamic nature of the articulators during speech. Simulating the movement of the tongue, velum, and pharynx is often limited by image segmentation obstacles, where, movements of the velar structures are segmented through manual tracking. These methods are extremely time-consuming, coupled with inherent noise, motion artifacts, air interfaces, and refractions often complicate the process of computer-based automatic tracking. Furthermore, image segmentation and processing techniques of velopharyngeal structures often suffer from leakage issues related to the poor image quality of the MRI and the lack of recognizable boundaries between the velum and pharynx during contact moments. Computer-based tracking algorithms are developed to overcome these disadvantages by utilizing machine learning techniques and corresponding speech signals that may be considered prior information. The purpose of this study is to illustrate a methodology to track the velum and pharynx from a MRI sequence using the Hidden Markov Model (HMM) and Mel-Frequency Cepstral Coefficients (MFCC) by analyzing the corresponding audio signals. Auditory models such as MFCC have been widely used in Automatic Speech Recognition (ASR) systems. Our method uses customized version of the traditional approach for audio feature extraction in order to extract visual feature from the outer boundaries of the velum and the pharynx marked (selected pixel) by a novel method, The reduced audio features helps to shrink the search space of HMM and improve the system performance.   Three hundred consecutive images were tagged by the researcher. Two hundred of these images and the corresponding audio features (5 seconds) were used to train the HMM and a 2.5 second long audio file was used to test the model. The error rate was measured by calculating minimum distance between predicted and actual markers. Our model was able to track and animate dynamic articulators during the speech process in real-time with an overall accuracy of 81% considering one pixel threshold. The predicted markers (pixels) indicated the segmented structures, even though the contours of contacted areas were fuzzy and unrecognizable.  M.S

Volumetric Assessment of Extratemporal Structures in Patients With Temporal Lobe Epilepsy

Background: We assessed the presence of brain volume loss in the extratemporal structures in patients with temporal lobe epilepsy (TLE). The associations between brain volume loss in these structures and epilepsy duration, magnetic resonance imaging (MRI) findings, and occurrence of focal to bilateral tonic-clonic seizures (TCS) were assessed. Methods: In this cross-sectional study, all adult patients with drug-resistant TLE, who were admitted to the epilepsy monitoring unit at Loghman-Hakim Hospital, Tehran, Iran, during 2016-2020, were included. For all the participants, brain MRI was performed and patients with TLE were divided into two subgroups of those with hippocampal sclerosis (TLE-HS) and patients with normal-appearing brain MRI findings (TLE-no). Independent sample t test was applied to compare quantitative variables in the study groups. Pearson correlation test examined the correlation between the clinical and volumetric features. Results: 203 participants (81 patients with TLE and 122 healthy controls) were studied. Compared with healthy controls, patients with TLE showed a decrease in their midbrain (P = 0.02) and thalamus (P = 0.01) volume. The degree of thalamic atrophy was more significant in TLE-HS (P = 0.03). Moreover, the degree of midbrain volume loss was more significant (P = 0.07) in patients who had TCS in the past two years (N = 31) compared with those who did not (N = 50). The volume of the thalamus (r: -0.252, P = 0.02) and pallidum (r: -0.255, P = 0.02) had inverse correlations with the epilepsy duration. Conclusion: Patients with TLE have lower midbrain and thalamus volume compared with the healthy controls, which may be attributed to the seizure-induced injury. Midbrain atrophy may theoretically increase the risk of sudden unexpected death in epilepsy (SUDEP) because of the enhanced autonomic dysfunction

Interferon beta-1a as a Candidate for COVID-19 Treatment; An Open-Label Single-Arm Clinical Trial

Introduction: Since December 2019, an outbreak of Covid-19 has caused growing concern in multiple countries. Researchers around the world are working to find a treatment or a vaccine for Covid-19 and different treatment approaches have been tested in this regard. Objective: This study was designed and conducted to assess the possible efficacy of Interferon beta-1a as a safe and efficient candidate for Covid-19 treatment. Methods: This is an investigator-initiated, open-label, single-arm clinical trial. Twenty patients with suspected Covid-19, who were admitted to Sina hospital in Tehran, Iran, with moderate to severe symptoms, from 6 to 10 March, 2020, were enrolled. Patients were treated with antiviral and hydroxychloroquine combination therapy, along with subcutaneous Interferon beta-1a for 5 consecutive days. Baseline characteristics and findings during the course of admission and 5 days after discharge were recorded for all the patients. Results: In total, 20 patients with suspected Covid-19 were included in this study, 12 (60%) of which were male. The median (Interquartile (IQ) range) of patients’ age was 55.5 (43-63.5). The most common symptom of the patients at onset of disease was fever. The median (IQ range) of duration of hospital stay was 5.0 (3-6) days. Only 2 cases were admitted to ICU. At the time of follow-up, 15 (94%) patients reported that they generally felt good and had oral tolerance, 1 patient had suffered from dyspnea, 5 patients had suffered from cough, none of them had experienced fever and no case of re-admission or death was reported after discharge. Conclusions: Results of the current study are in favor of using Interferon beta-1a in addition to recommended antiviral treatment in Covid-19 patients

Interferon beta-1a as a Candidate for COVID-19 Treatment; An Open-Label Single-Arm Clinical Trial

Introduction: Since December 2019, an outbreak of Covid-19 has caused growing concern in multiple countries. Researchers around the world are working to find a treatment or a vaccine for Covid-19 and different treatment approaches have been tested in this regard. Objective: This study was designed and conducted to assess the possible efficacy of Interferon beta-1a as a safe and efficient candidate for Covid-19 treatment. Methods: This is an investigator-initiated, open-label, single-arm clinical trial. Twenty patients with suspected Covid-19, who were admitted to Sina hospital in Tehran, Iran, with moderate to severe symptoms, from 6 to 10 March, 2020, were enrolled. Patients were treated with antiviral and hydroxychloroquine combination therapy, along with subcutaneous Interferon beta-1a for 5 consecutive days. Baseline characteristics and findings during the course of admission and 5 days after discharge were recorded for all the patients. Results: In total, 20 patients with suspected Covid-19 were included in this study, 12 (60%) of which were male. The median (Interquartile (IQ) range) of patients’ age was 55.5 (43-63.5). The most common symptom of the patients at onset of disease was fever. The median (IQ range) of duration of hospital stay was 5.0 (3-6) days. Only 2 cases were admitted to ICU. At the time of follow-up, 15 (94%) patients reported that they generally felt good and had oral tolerance, 1 patient had suffered from dyspnea, 5 patients had suffered from cough, none of them had experienced fever and no case of re-admission or death was reported after discharge. Conclusions: Results of the current study are in favor of using Interferon beta-1a in addition to recommended antiviral treatment in Covid-19 patients

Using Synchronized Audio Mapping to Predict Velar and Pharyngeal Wall Locations during Dynamic MRI Sequences

Automatic tongue, velum (i.e., soft palate), and pharyngeal movement tracking systems provide a significant benefit for the analysis of dynamic speech movements. Studies have been conducted using ultrasound, x-ray, and Magnetic Resonance Images (MRI) to examine the dynamic nature of the articulators during speech. Simulating the movement of the tongue, velum, and pharynx is often limited by image segmentation obstacles, where, movements of the velar structures are segmented through manual tracking. These methods are extremely time-consuming, coupled with inherent noise, motion artifacts, air interfaces, and refractions often complicate the process of computer-based automatic tracking. Furthermore, image segmentation and processing techniques of velopharyngeal structures often suffer from leakage issues related to the poor image quality of the MRI and the lack of recognizable boundaries between the velum and pharynx during contact moments. Computer-based tracking algorithms are developed to overcome these disadvantages by utilizing machine learning techniques and corresponding speech signals that may be considered prior information. The purpose of this study is to illustrate a methodology to track the velum and pharynx from a MRI sequence using the Hidden Markov Model (HMM) and Mel-Frequency Cepstral Coefficients (MFCC) by analyzing the corresponding audio signals. Auditory models such as MFCC have been widely used in Automatic Speech Recognition (ASR) systems. Our method uses customized version of the traditional approach for audio feature extraction in order to extract visual feature from the outer boundaries of the velum and the pharynx marked (selected pixel) by a novel method, The reduced audio features helps to shrink the search space of HMM and improve the system performance. Three hundred consecutive images were tagged by the researcher. Two hundred of these images and the corresponding audio features (5 seconds) were used to train the HMM and a 2.5 second long audio file was used to test the model. The error rate was measured by calculating minimum distance between predicted and actual markers. Our model was able to track and animate dynamic articulators during the speech process in real-time with an overall accuracy of 81% considering one pixel threshold. The predicted markers (pixels) indicated the segmented structures, even though the contours of contacted areas were fuzzy and unrecognizable

Using Synchronized Audio Mapping to Track and Predict Velar and Pharyngeal Wall Locations during Dynamic MRI Sequences

Purpose: The purpose of this study is to demonstrate a novel innovative computational modeling technique to 1) track velar and pharyngeal wall movement from dynamic MRI data and to 2) examine the utility of using recorded participant audio signals to estimate velar and pharyngeal wall movement during a speech task. A series of dynamic MRI data and audio acoustic features were used to develop and inform a Hidden Markov Model (HMM) and Mel-Frequency Cepstral Coefficients (MFCC) model.Methods: One adult male subject was imaged using a fast-gradient echo Fast Low Angle Shot (FLASH) multi-shot spiral technique to acquire 15.8 frames per second (fps) of the midsagittal image plane during the production of “ansa.†The nasal surface of the velum and the posterior pharyngeal wall was identified and marked using a novel pixel selection method. The error rate was measured by calculating the accumulation error and through visual inspection.Results: The proposed model traced and animated dynamic articulators during the speech process in real-time with an overall accuracy of 81% considering one pixel threshold. The predicted markers (pixels) segmented the structures of interest in the velopharyngeal area and were able to successfully predict the velar and pharyngeal configurations when provided with the audio signal.Conclusion: This study demonstrates a novel and innovative approach to tracking dynamic velopharyngeal movements. Discussion of the potential application of a predictive model that relies on audio signals to detect the presence of a velopharyngeal gap is discussed

How does COVID-19 vaccination affect long-COVID symptoms?

ObjectiveThe current study aimed to identify the association between COVID-19 vaccination and prolonged post-COVID symptoms (long-COVID) in adults who reported suffering from this condition.MethodsThis was a retrospective follow-up study of adults with long-COVID syndrome. The data were collected during a phone call to the participants in January-February 2022. We inquired about their current health status and also their vaccination status if they agreed to participate.ResultsIn total, 1236 people were studied; 543 individuals reported suffering from long long- COVID (43.9%). Chi square test showed that 15 out of 51 people (29.4%) with no vaccination and 528 out of 1185 participants (44.6%) who received at least one dose of any vaccine had long long- COVID symptoms (p = 0.032).ConclusionsIn people who have already contracted COVID-19 and now suffer from long-COVID, receiving a COVID vaccination has a significant association with prolonged symptoms of long-COVID for more than one year after the initial infection. However, vaccines reduce the risk of severe COVID-19 (including reinfections) and its catastrophic consequences (e.g., death). Therefore, it is strongly recommended that all people, even those with a history of COVID-19, receive vaccines to protect themselves against this fatal viral infection

STROBE statement-checklist of items that should be included in reports of <i>cross-sectional studies</i>.

STROBE statement-checklist of items that should be included in reports of cross-sectional studies.</p

Long long-COVID symptoms in association with the vaccination status of the participants^b'*'.

Long long-COVID symptoms in association with the vaccination status of the participantsb'*'.</p